Semantic Classification in Aerial Imagery by Integrating Appearance and Height Information

In this paper we present an efficient technique to obtain accurate semantic classification on the pixel level capable of integrating various modalities, such as color, edge responses, and height information. We propose a novel feature representation based on Sigma Points computations that enables a simple application of powerful covariance descriptors to a multi-class randomized forest framework. Additionally, we include semantic contextual knowledge using a conditional random field formulation. In order to achieve a fair comparison to state-of-the-art methods our approach is first evaluated on the MSRC image collection and is then demonstrated on three challenging aerial image datasets Dallas, Graz, and San Francisco. We obtain a full semantic classification on single aerial images within two minutes. Moreover, the computation time on large scale imagery including hundreds of images is investigated

3D Reconstruction Using an n-Layer Heightmap

Abstract. We present a novel method for 3D reconstruction of urban scenes extending a recently introduced heightmap model. Our model has several advantages for 3D modeling of urban scenes: it naturally en-forces vertical surfaces, has no holes, leads to an efficient algorithm, and is compact in size. We remove the major limitation of the heightmap by enabling modeling of overhanging structures. Our method is based on an an n-layer heightmap with each layer representing a surface between full and empty space. The configuration of layers can be computed optimally using a dynamic programming method. Our cost function is derived from probabilistic occupancy, and incorporates the Bayesian Information Cri-terion (BIC) for selecting the number of layers to use at each pixel. 3D surface models are extracted from the heightmap. We show results from a variety of datasets including Internet photo collections. Our method runs on the GPU and the complete system processes video at 13 Hz.

Unsupervised Facade Segmentation using Repetitive Patterns

Abstract. We introduce a novel approach for separating and segmenting individual facades from streetside images. Our algorithm incorporates prior knowledge about arbitrarily shaped repetitive regions which are detected using intensity profile descriptors and a voting–based matcher. In the experiments we compare our approach to extended state–of–the–art matching approaches using more than 600 challenging streetside images, including different building styles and various occlusions. Our algorithm outperforms these approaches and allows to correctly separate 94 % of the facades. Pixel–wise comparison to our ground–truth yields a segmentation accuracy of 85%. According to these results our work is an important contribution to fully automatic building reconstruction.

Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization

We propose a method for urban 3D reconstruction, which incorporates semantic information and plane priors within the reconstruction process in order to generate visually appealing 3D models. We introduce a plane detection algorithm using 3D lines, which detects a more complete and less spurious plane set compared to point-based methods in urban environments. Further, the proposed normalized visibility-based energy formulation eases the combination of several energy terms within a tetrahedra occupancy labeling algorithm and, hence, is well suited for combining it with class specific smoothness terms. As a result, we produce visually appealing and detailed building models (i.e., straight edges and planar surfaces) and a smooth reconstruction of the surroundings

A variational approach to semiautomatic generation of digital terrain models

Abstract. We present a semiautomatic approach to generate high quality digital terrain models (DTM) from digital surface models (DSM). A DTM is a model of the earths surface, where all man made objects and the vegetation have been removed. In order to achieve this, we use a variational energy minimization approach. The proposed energy functional incorporates Huber regularization to yield piecewise smooth surfaces and an L1 norm in the data fidelity term. Additionally, a minimum constraint is used in order to prevent the ground level from pulling up, while buildings and vegetation are pulled down. Being convex, the proposed formulation allows us to compute the globally optimal solution. Clearly, a fully automatic approach does not yield the desired result in all situations. Therefore, we additionally allow the user to affect the algorithm using different user interaction tools. Furthermore, we provide a real-time 3D visualization of the output of the algorithm which additionally helps the user to assess the final DTM. We present results of the proposed approach using several real data sets.

ULTRAMAP V3 – A REVOLUTION IN AERIAL PHOTOGRAMMETRY

In the last years, Microsoft has driven innovation in the aerial photogrammetry community. Besides the market leading camera technology, UltraMap has grown to an outstanding photogrammetric workflow system which enables users to effectively work with large digital aerial image blocks in a highly automated way. Best example is the project-based color balancing approach which automatically balances images to a homogeneous block. UltraMap V3 continues innovation, and offers a revolution in terms of ortho processing. A fully automated dense matching module strives for high precision digital surface models (DSMs) which are calculated either on CPUs or on GPUs using a distributed processing framework. By applying constrained filtering algorithms, a digital terrain model can be derived which in turn can be used for fully automated traditional ortho texturing. By having the knowledge about the underlying geometry, seamlines can be generated automatically by applying cost functions in order to minimize visual disturbing artifacts. By exploiting the generated DSM information, a DSMOrtho is created using the balanced input images. Again, seamlines are detected automatically resulting in an automatically balanced ortho mosaic. Interactive block-based radiometric adjustments lead to a high quality ortho product based on UltraCam imagery. UltraMap v3 is the first fully integrated and interactive solution for supporting UltraCam images at best in order to deliver DSM and ortho imagery

How aligned are the perspectives of EU regulators and HTA bodies? A comparative analysis of regulatory-HTA parallel scientific advice

BackgroundIn 2010, the European Medicines Agency (EMA) initiated a pilot project on parallel scientific advice with Health Technology Assessment bodies (HTABs) that allows manufacturers to receive simultaneous feedback from both the European Union (EU) regulators and HTABs on their development plans for medicines.AimsThe present retrospective qualitative analysis aimed to explore how the parallel scientific advice system is working and levels of commonality between the EU regulators and HTABs, and among HTABs, when applicants obtain parallel scientific advice from both a regulatory and an HTA perspective.MethodsWe analysed the minutes of discussion meetings held at the EMA between 2010, when parallel advice was launched, and 1 May 2015, when the cutoff date for data extraction was set. The analysis was based on predefined criteria and conducted at two different levels of comparison: the answers of the HTABs vs. those of the regulators, and between the answers of the participating HTA agencies.ResultsThe analysis was based on 31 procedures of parallel scientific advice. The level of full agreements was highest for questions on patient population (77%), while disagreements reached a peak for questions on the study comparator (30%). With regard to comparisons among HTABs, there was a high level of agreement for all domains.ConclusionsThere is evident commonality, in terms of evidence requirements between the EU regulators and participating HTABs, as well as among HTABs, on most aspects of clinical development. Indeed, regardless of the question content, the analysis showed that a high level of overall agreement was reached through the process of parallel scientific advice

The impact of parallel regulatory–health technology assessment scientific advice on clinical development. Assessing the uptake of regulatory and health technology assessment recommendations

AimsThe parallel regulatory-health technology assessment scientific advice (PSA) procedure allows manufacturers to receive simultaneous feedback from both EU regulators and health technology assessment (HTA) bodies on development plans for new medicines. The primary objective of the present study is to investigate whether PSA is integrated in the clinical development programmes for which advice was sought.MethodsContents of PSA provided by regulators and HTA bodies for each procedure between 2010 and 2015 were analysed. The development of all clinical studies for which PSA had been sought was tracked using three different databases. The rate of uptake of the advice provided by regulators and HTA bodies was assessed on two key variables: comparator/s and primary endpoint.ResultsIn terms of uptake of comparator recommendations at the time of PSA in the actual development, our analysis showed that manufacturers implemented comparators to address both the needs of regulators and of at least one HTA body in 12 of 21 studies. For primary endpoints, in all included studies manufacturers addressed both the needs of the regulators and at least one HTA body.ConclusionsOne of the key findings of this analysis is that manufacturers tend to implement changes to the development programme based on both regulatory and HTA advice with regards to the choice of primary endpoint and comparator. It also confirms the challenging choice of the study comparator, for which manufacturers seem to be more inclined to satisfy the regulatory advice. Continuous research efforts in this area are of paramount importance from a public health perspective